{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default_exp models.TSTPlus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TSTPlus (Time Series Transformer)\n",
    "\n",
    "> This is an unofficial PyTorch implementation created by Ignacio Oguiza (timeseriesAI@gmail.com) based on TST (Zerveas, 2020) and Transformer (Vaswani, 2017)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**References:**\n",
    "\n",
    "This is an unofficial PyTorch implementation by Ignacio Oguiza of  - oguiza@gmail.com based on:\n",
    "* George Zerveas et al. A Transformer-based Framework for Multivariate Time Series Representation Learning, in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), August 14--18, 2021. ArXiV version: https://arxiv.org/abs/2010.02803\n",
    "* Official implementation: https://github.com/gzerveas/mvts_transformer\n",
    "\n",
    "```bash\n",
    "@inproceedings{10.1145/3447548.3467401,\n",
    "author = {Zerveas, George and Jayaraman, Srideepika and Patel, Dhaval and Bhamidipaty, Anuradha and Eickhoff, Carsten},\n",
    "title = {A Transformer-Based Framework for Multivariate Time Series Representation Learning},\n",
    "year = {2021},\n",
    "isbn = {9781450383325},\n",
    "publisher = {Association for Computing Machinery},\n",
    "address = {New York, NY, USA},\n",
    "url = {https://doi.org/10.1145/3447548.3467401},\n",
    "doi = {10.1145/3447548.3467401},\n",
    "booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining},\n",
    "pages = {2114–2124},\n",
    "numpages = {11},\n",
    "keywords = {regression, framework, multivariate time series, classification, transformer, deep learning, self-supervised learning, unsupervised learning, imputation},\n",
    "location = {Virtual Event, Singapore},\n",
    "series = {KDD '21}\n",
    "}\n",
    "```\n",
    "\n",
    "* Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). [Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).](https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf)\n",
    "\n",
    "* He, R., Ravula, A., Kanagal, B., & Ainslie, J. (2020). Realformer: Transformer Likes Informed Attention. arXiv preprint arXiv:2012.11747.\n",
    "\n",
    "This implementation is adapted to work with the rest of the `tsai` library, and contain some hyperparameters that are not available in the original implementation. I included them for experimenting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from tsai.imports import *\n",
    "from tsai.utils import *\n",
    "from tsai.models.layers import *\n",
    "from tsai.models.utils import *\n",
    "from tsai.data.core import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Positional encoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def PositionalEncoding(q_len, d_model, normalize=True):\n",
    "    pe = torch.zeros(q_len, d_model, device=default_device())\n",
    "    position = torch.arange(0, q_len).unsqueeze(1)\n",
    "    div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model))\n",
    "    pe[:, 0::2] = torch.sin(position * div_term)\n",
    "    pe[:, 1::2] = torch.cos(position * div_term)\n",
    "    if normalize:\n",
    "        pe = pe - pe.mean()\n",
    "        pe = pe / (pe.std() * 10) \n",
    "    return pe.to(device=device)\n",
    "\n",
    "SinCosPosEncoding = PositionalEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(3.2037498e-10, 0.09999991, -0.18388666, 0.11518021, (1000, 512))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pe = PositionalEncoding(1000, 512).detach().cpu().numpy()\n",
    "plt.pcolormesh(pe, cmap='viridis')\n",
    "plt.title('PositionalEncoding')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "pe.mean(), pe.std(), pe.min(), pe.max(), pe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True, eps=1e-3, verbose=False, device=default_device()):\n",
    "    x = .5 if exponential else 1\n",
    "    i = 0\n",
    "    for i in range(100):\n",
    "        cpe = 2 * (torch.linspace(0, 1, q_len).reshape(-1, 1) ** x) * (torch.linspace(0, 1, d_model).reshape(1, -1) ** x) - 1\n",
    "        pv(f'{i:4.0f}  {x:5.3f}  {cpe.mean():+6.3f}', verbose)\n",
    "        if abs(cpe.mean()) <= eps: break\n",
    "        elif cpe.mean() > eps: x += .001\n",
    "        else: x -= .001\n",
    "        i += 1\n",
    "    if normalize:\n",
    "        cpe = cpe - cpe.mean()\n",
    "        cpe = cpe / (cpe.std() * 10) \n",
    "    return cpe.to(device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(3.695488e-09, 0.09999991, -0.22459325, 0.22487777)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpe = Coord2dPosEncoding(1000, 512, exponential=True, normalize=True).cpu().numpy()\n",
    "plt.pcolormesh(cpe, cmap='viridis')\n",
    "plt.title('Coord2dPosEncoding')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "plt.plot(cpe.mean(0))\n",
    "plt.show()\n",
    "plt.plot(cpe.mean(1))\n",
    "plt.show()\n",
    "cpe.mean(), cpe.std(), cpe.min(), cpe.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def Coord1dPosEncoding(q_len, exponential=False, normalize=True, device=default_device()):\n",
    "    cpe = (2 * (torch.linspace(0, 1, q_len).reshape(-1, 1)**(.5 if exponential else 1)) - 1)\n",
    "    if normalize:\n",
    "        cpe = cpe - cpe.mean()\n",
    "        cpe = cpe / (cpe.std() * 10) \n",
    "    return cpe.to(device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 0.099949986, -0.2820423, 0.14113107, (1000, 1))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpe = Coord1dPosEncoding(1000, exponential=True, normalize=True).detach().cpu().numpy()\n",
    "plt.pcolormesh(cpe, cmap='viridis')\n",
    "plt.title('Coord1dPosEncoding')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "plt.plot(cpe.mean(1))\n",
    "plt.show()\n",
    "cpe.mean(), cpe.std(), cpe.min(), cpe.max(), cpe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 0.099949986, -0.2820423, 0.14113107)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpe = Coord1dPosEncoding(1000, exponential=True, normalize=True).detach().cpu().numpy()\n",
    "plt.pcolormesh(cpe, cmap='viridis')\n",
    "plt.title('Coord1dPosEncoding')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "plt.plot(cpe.mean(1))\n",
    "plt.show()\n",
    "cpe.mean(), cpe.std(), cpe.min(), cpe.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporti\n",
    "def get_activation_fn(activation):\n",
    "    if callable(activation): return activation()\n",
    "    elif activation.lower() == \"relu\": return nn.ReLU()\n",
    "    elif activation.lower() == \"gelu\": return nn.GELU()\n",
    "    raise ValueError(f'{activation} is not available. You can use \"relu\", \"gelu\", or a callable')\n",
    "    \n",
    "class _TSTEncoderLayer(Module):\n",
    "    def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=256, store_attn=False,\n",
    "                 norm='BatchNorm', attn_dropout=0, dropout=0., bias=True, activation=\"gelu\", res_attention=False, pre_norm=False):\n",
    "\n",
    "        assert not d_model%n_heads, f\"d_model ({d_model}) must be divisible by n_heads ({n_heads})\"\n",
    "        d_k = ifnone(d_k, d_model // n_heads)\n",
    "        d_v = ifnone(d_v, d_model // n_heads)\n",
    "\n",
    "        # Multi-Head attention\n",
    "        self.res_attention = res_attention\n",
    "        self.self_attn = MultiheadAttention(d_model, n_heads, d_k, d_v, attn_dropout=attn_dropout, proj_dropout=dropout, res_attention=res_attention)\n",
    "\n",
    "        # Add & Norm\n",
    "        self.dropout_attn = nn.Dropout(dropout)\n",
    "        if \"batch\" in norm.lower():\n",
    "            self.norm_attn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))\n",
    "        else: \n",
    "            self.norm_attn = nn.LayerNorm(d_model)\n",
    "\n",
    "        # Position-wise Feed-Forward\n",
    "        self.ff = nn.Sequential(nn.Linear(d_model, d_ff, bias=bias), \n",
    "                                get_activation_fn(activation), \n",
    "                                nn.Dropout(dropout), \n",
    "                                nn.Linear(d_ff, d_model, bias=bias))\n",
    "\n",
    "        # Add & Norm\n",
    "        self.dropout_ffn = nn.Dropout(dropout)\n",
    "        if \"batch\" in norm.lower():\n",
    "            self.norm_ffn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))\n",
    "        else: \n",
    "            self.norm_ffn = nn.LayerNorm(d_model)\n",
    "        \n",
    "        self.pre_norm = pre_norm\n",
    "        self.store_attn = store_attn\n",
    "\n",
    "    def forward(self, src:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None) -> Tensor:\n",
    "\n",
    "        # Multi-Head attention sublayer\n",
    "        if self.pre_norm:\n",
    "            src = self.norm_attn(src)\n",
    "        ## Multi-Head attention\n",
    "        if self.res_attention:\n",
    "            src2, attn, scores = self.self_attn(src, src, src, prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask)\n",
    "        else:\n",
    "            src2, attn = self.self_attn(src, src, src, key_padding_mask=key_padding_mask, attn_mask=attn_mask)\n",
    "        if self.store_attn: \n",
    "            self.attn = attn\n",
    "        ## Add & Norm\n",
    "        src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout\n",
    "        if not self.pre_norm:\n",
    "            src = self.norm_attn(src)\n",
    "\n",
    "        # Feed-forward sublayer\n",
    "        if self.pre_norm:\n",
    "            src = self.norm_ffn(src)\n",
    "        ## Position-wise Feed-Forward\n",
    "        src2 = self.ff(src)\n",
    "        ## Add & Norm\n",
    "        src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout\n",
    "        if not self.pre_norm:\n",
    "            src = self.norm_ffn(src)\n",
    "\n",
    "        if self.res_attention:\n",
    "            return src, scores\n",
    "        else:\n",
    "            return src"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attn_mask torch.Size([50, 50]) key_padding_mask torch.Size([16, 50])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 50, 128])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = torch.rand(16, 50, 128)\n",
    "attn_mask = torch.triu(torch.ones(50, 50)) # shape: q_len x q_len\n",
    "key_padding_mask = torch.zeros(16, 50)\n",
    "key_padding_mask[[1, 3, 6, 15], -10:] = 1\n",
    "key_padding_mask = key_padding_mask.bool()\n",
    "print('attn_mask', attn_mask.shape, 'key_padding_mask', key_padding_mask.shape)\n",
    "encoder = _TSTEncoderLayer(q_len=50, d_model=128, n_heads=8, d_k=None, d_v=None, d_ff=512, attn_dropout=0., dropout=0.1, store_attn=True, activation='gelu')\n",
    "output = encoder(t, key_padding_mask=key_padding_mask, attn_mask=attn_mask)\n",
    "output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap='viridis'\n",
    "figsize=(6,5)\n",
    "plt.figure(figsize=figsize)\n",
    "plt.pcolormesh(encoder.attn[0][0].detach().cpu().numpy(), cmap=cmap)\n",
    "plt.title('Self-attention map')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporti\n",
    "class _TSTEncoder(Module):\n",
    "    def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=None, norm='BatchNorm', attn_dropout=0., dropout=0., activation='gelu', \n",
    "                 res_attention=False, n_layers=1, pre_norm=False, store_attn=False):\n",
    "        self.layers = nn.ModuleList([_TSTEncoderLayer(q_len, d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, \n",
    "                                                      attn_dropout=attn_dropout, dropout=dropout, \n",
    "                                                      activation=activation, res_attention=res_attention, \n",
    "                                                      pre_norm=pre_norm, store_attn=store_attn) for i in range(n_layers)])\n",
    "        self.res_attention = res_attention\n",
    "\n",
    "    def forward(self, src:Tensor, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None):\n",
    "        output = src\n",
    "        scores = None\n",
    "        if self.res_attention:\n",
    "            for mod in self.layers: output, scores = mod(output, prev=scores, key_padding_mask=key_padding_mask, attn_mask=attn_mask)\n",
    "            return output\n",
    "        else:\n",
    "            for mod in self.layers: output = mod(output, key_padding_mask=key_padding_mask, attn_mask=attn_mask)\n",
    "            return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporti\n",
    "class _TSTBackbone(Module):\n",
    "    def __init__(self, c_in, seq_len, max_seq_len=512,\n",
    "                 n_layers=3, d_model=128, n_heads=16, d_k=None, d_v=None,\n",
    "                 d_ff=256, norm='BatchNorm', attn_dropout=0., dropout=0., act=\"gelu\", store_attn=False,\n",
    "                 key_padding_mask='auto', padding_var=None, attn_mask=None, res_attention=True, pre_norm=False,\n",
    "                 pe='zeros', learn_pe=True, verbose=False, **kwargs):\n",
    "\n",
    "        # Input encoding\n",
    "        q_len = seq_len\n",
    "        self.new_q_len = False\n",
    "        if max_seq_len is not None and seq_len > max_seq_len: # Control temporal resolution\n",
    "            self.new_q_len = True\n",
    "            q_len = max_seq_len\n",
    "            tr_factor = math.ceil(seq_len / q_len)\n",
    "            total_padding = (tr_factor * q_len - seq_len)\n",
    "            padding = (total_padding // 2, total_padding - total_padding // 2)\n",
    "            self.W_P = nn.Sequential(Pad1d(padding), Conv1d(c_in, d_model, kernel_size=tr_factor, padding=0, stride=tr_factor))\n",
    "            pv(f'temporal resolution modified: {seq_len} --> {q_len} time steps: kernel_size={tr_factor}, stride={tr_factor}, padding={padding}.\\n', verbose)\n",
    "        elif kwargs:\n",
    "            self.new_q_len = True\n",
    "            t = torch.rand(1, 1, seq_len)\n",
    "            q_len = Conv1d(1, 1, **kwargs)(t).shape[-1]\n",
    "            self.W_P = Conv1d(c_in, d_model, **kwargs) # Eq 2\n",
    "            pv(f'Conv1d with kwargs={kwargs} applied to input to create input encodings\\n', verbose)\n",
    "        else:\n",
    "            self.W_P = nn.Linear(c_in, d_model)        # Eq 1: projection of feature vectors onto a d-dim vector space\n",
    "        self.seq_len = q_len\n",
    "\n",
    "        # Positional encoding\n",
    "        self.W_pos = self._positional_encoding(pe, learn_pe, q_len, d_model)\n",
    "\n",
    "        # Residual dropout\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "        # Encoder\n",
    "        self.encoder = _TSTEncoder(q_len, d_model, n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, dropout=dropout, \n",
    "                                   pre_norm=pre_norm, activation=act, res_attention=res_attention, n_layers=n_layers, store_attn=store_attn)\n",
    "        self.transpose = Transpose(-1, -2, contiguous=True)\n",
    "        self.key_padding_mask, self.padding_var, self.attn_mask = key_padding_mask, padding_var, attn_mask\n",
    "\n",
    "    def forward(self, inp) -> Tensor:  \n",
    "        r\"\"\"Pass the input through the TST backbone.\n",
    "        Args:\n",
    "            inp: input (optionally with padding mask. 1s (meaning padded) in padding mask will be ignored while 0s (non-padded) will be unchanged.)\n",
    "        Shape:\n",
    "            There are 3 options: \n",
    "            1. inp: Tensor containing just time series data [bs x nvars x q_len] \n",
    "            2. inp: Tensor containing time series data plus a padding feature in the last channel [bs x (nvars + 1) x q_len]\n",
    "            3. inp: tuple containing a tensor with time series data plus a padding mask per batch ([bs x nvars x q_len] , [bs x q_len] )\n",
    "        \"\"\"\n",
    "\n",
    "        # x and padding mask\n",
    "        if isinstance(inp, tuple): x, key_padding_mask = inp\n",
    "        elif self.key_padding_mask == 'auto': x, key_padding_mask = self._key_padding_mask(inp) # automatically identify padding mask\n",
    "        elif self.key_padding_mask == -1: x, key_padding_mask = inp[:, :-1], inp[:, -1]         # padding mask is the last channel\n",
    "        else: x, key_padding_mask = inp, None\n",
    "\n",
    "        # Input encoding\n",
    "        if self.new_q_len: u = self.W_P(x).transpose(2,1) # Eq 2        # u: [bs x d_model x q_len] transposed to [bs x q_len x d_model]\n",
    "        else: u = self.W_P(x.transpose(2,1))              # Eq 1        # u: [bs x q_len x nvars] converted to [bs x q_len x d_model]\n",
    "\n",
    "        # Positional encoding\n",
    "        u = self.dropout(u + self.W_pos)\n",
    "\n",
    "        # Encoder\n",
    "        z = self.encoder(u, key_padding_mask=key_padding_mask, attn_mask=self.attn_mask)    # z: [bs x q_len x d_model]\n",
    "        z = self.transpose(z)                                                               # z: [bs x d_model x q_len]\n",
    "        if key_padding_mask is not None: \n",
    "            z = z * torch.logical_not(key_padding_mask.unsqueeze(1))  # zero-out padding embeddings\n",
    "        return z\n",
    "\n",
    "    def _positional_encoding(self, pe, learn_pe, q_len, d_model):\n",
    "        # Positional encoding\n",
    "        if pe == None:\n",
    "            W_pos = torch.empty((q_len, d_model), device=default_device()) # pe = None and learn_pe = False can be used to measure impact of pe\n",
    "            nn.init.uniform_(W_pos, -0.02, 0.02)\n",
    "            learn_pe = False\n",
    "        elif pe == 'zero': \n",
    "            W_pos = torch.empty((q_len, 1), device=default_device())\n",
    "            nn.init.uniform_(W_pos, -0.02, 0.02)\n",
    "        elif pe == 'zeros': \n",
    "            W_pos = torch.empty((q_len, d_model), device=default_device())\n",
    "            nn.init.uniform_(W_pos, -0.02, 0.02)\n",
    "        elif pe == 'normal' or pe == 'gauss':\n",
    "            W_pos = torch.zeros((q_len, 1), device=default_device())\n",
    "            torch.nn.init.normal_(W_pos, mean=0.0, std=0.1)\n",
    "        elif pe == 'uniform':\n",
    "            W_pos = torch.zeros((q_len, 1), device=default_device())\n",
    "            nn.init.uniform_(W_pos, a=0.0, b=0.1)\n",
    "        elif pe == 'lin1d': W_pos = Coord1dPosEncoding(q_len, exponential=False, normalize=True)\n",
    "        elif pe == 'exp1d': W_pos = Coord1dPosEncoding(q_len, exponential=True, normalize=True)\n",
    "        elif pe == 'lin2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True)\n",
    "        elif pe == 'exp2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=True, normalize=True)\n",
    "        elif pe == 'sincos': W_pos = PositionalEncoding(q_len, d_model, normalize=True)\n",
    "        else: raise ValueError(f\"{pe} is not a valid pe (positional encoder. Available types: 'gauss'=='normal', \\\n",
    "            'zeros', 'zero', uniform', 'lin1d', 'exp1d', 'lin2d', 'exp2d', 'sincos', None.)\")\n",
    "        return nn.Parameter(W_pos, requires_grad=learn_pe)\n",
    "\n",
    "    def _key_padding_mask(self, x):\n",
    "        if self.padding_var is not None:\n",
    "            mask = TSMaskTensor(x[:, self.padding_var] == 1)            # key_padding_mask: [bs x q_len]\n",
    "            return x, mask\n",
    "        else:\n",
    "            mask = torch.isnan(x)\n",
    "            x[mask] = 0\n",
    "            if mask.any():\n",
    "                mask = TSMaskTensor((mask.float().mean(1)==1).bool())   # key_padding_mask: [bs x q_len]\n",
    "                return x, mask\n",
    "            else:\n",
    "                return x, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export\n",
    "class TSTPlus(nn.Sequential):\n",
    "    \"\"\"TST (Time Series Transformer) is a Transformer that takes continuous time series as inputs\"\"\"\n",
    "    def __init__(self, c_in:int, c_out:int, seq_len:int, max_seq_len:Optional[int]=512,\n",
    "                 n_layers:int=3, d_model:int=128, n_heads:int=16, d_k:Optional[int]=None, d_v:Optional[int]=None,\n",
    "                 d_ff:int=256, norm:str='BatchNorm', attn_dropout:float=0., dropout:float=0., act:str=\"gelu\", key_padding_mask:bool='auto', \n",
    "                 padding_var:Optional[int]=None, attn_mask:Optional[Tensor]=None, res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False,\n",
    "                 pe:str='zeros', learn_pe:bool=True, flatten:bool=True, fc_dropout:float=0.,\n",
    "                 concat_pool:bool=False, bn:bool=False, custom_head:Optional=None,\n",
    "                 y_range:Optional[tuple]=None, verbose:bool=False, **kwargs):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            c_in: the number of features (aka variables, dimensions, channels) in the time series dataset.\n",
    "            c_out: the number of target classes.\n",
    "            seq_len: number of time steps in the time series.\n",
    "            max_seq_len: useful to control the temporal resolution in long time series to avoid memory issues. Default=512.\n",
    "            d_model: total dimension of the model (number of features created by the model). Default: 128 (range(64-512))\n",
    "            n_heads:  parallel attention heads. Default:16 (range(8-16)).\n",
    "            d_k: size of the learned linear projection of queries and keys in the MHA. Usual values: 16-512. Default: None -> (d_model/n_heads) = 32.\n",
    "            d_v: size of the learned linear projection of values in the MHA. Usual values: 16-512. Default: None -> (d_model/n_heads) = 32.\n",
    "            d_ff: the dimension of the feedforward network model. Default: 512 (range(256-512))\n",
    "            norm: flag to indicate whether BatchNorm (default) or LayerNorm is used in the encoder layers.\n",
    "            attn_dropout: dropout applied to the attention scores\n",
    "            dropout: amount of dropout applied to all linear layers except q,k&v projections in the encoder.\n",
    "            act: the activation function of intermediate layer, relu or gelu.\n",
    "            key_padding_mask:   a boolean padding mask will be applied to attention if 'auto' a mask to those steps in a sample where all features are nan.\n",
    "                                Other options include: True -->tuple (x, key_padding_mask), -1 --> key_padding_mask is the last channel, False: no mask.\n",
    "            padding_var: (optional) an int indicating the variable that contains the padded steps (0: non-padded, 1: padded). \n",
    "            attn_mask: a boolean mask will be applied to attention if a tensor of shape [min(seq_len, max_seq_len) x min(seq_len, max_seq_len)] if provided.\n",
    "            res_attention: if True Residual MultiheadAttention is applied.\n",
    "            pre_norm: if True normalization will be applied as the first step in the sublayers. Defaults to False\n",
    "            store_attn: can be used to visualize attention weights. Default: False.\n",
    "            n_layers: number of layers (or blocks) in the encoder. Default: 3 (range(1-4))\n",
    "            pe: type of positional encoder.\n",
    "                Available types (for experimenting): None, 'exp1d', 'lin1d', 'exp2d', 'lin2d', 'sincos', 'gauss' or 'normal',\n",
    "                'uniform', 'zero', 'zeros' (default, as in the paper).\n",
    "            learn_pe: learned positional encoder (True, default) or fixed positional encoder.\n",
    "            flatten: this will flatten the encoder output to be able to apply an mlp type of head (default=False)\n",
    "            fc_dropout: dropout applied to the final fully connected layer.\n",
    "            concat_pool: indicates if global adaptive concat pooling will be used instead of global adaptive pooling.\n",
    "            bn: indicates if batchnorm will be applied to the head.\n",
    "            custom_head: custom head that will be applied to the network. It must contain all kwargs (pass a partial function)\n",
    "            y_range: range of possible y values (used in regression tasks).\n",
    "            kwargs: nn.Conv1d kwargs. If not {}, a nn.Conv1d with those kwargs will be applied to original time series.\n",
    "        Input shape:\n",
    "            x: bs (batch size) x nvars (aka features, variables, dimensions, channels) x seq_len (aka time steps)\n",
    "            attn_mask: q_len x q_len\n",
    "            As mentioned in the paper, the input must be standardized by_var based on the entire training set.\n",
    "        \"\"\"\n",
    "        # Backbone\n",
    "        backbone = _TSTBackbone(c_in, seq_len=seq_len, max_seq_len=max_seq_len,\n",
    "                                n_layers=n_layers, d_model=d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, \n",
    "                                attn_dropout=attn_dropout, dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var,\n",
    "                                attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn, \n",
    "                                pe=pe, learn_pe=learn_pe, verbose=verbose, **kwargs)\n",
    "\n",
    "        # Head\n",
    "        self.head_nf = d_model\n",
    "        self.c_out = c_out\n",
    "        self.seq_len = backbone.seq_len\n",
    "        if custom_head: head = custom_head(self.head_nf, c_out, self.seq_len) # custom head passed as a partial func with all its kwargs\n",
    "        else: head = self.create_head(self.head_nf, c_out, self.seq_len, act=act, flatten=flatten, concat_pool=concat_pool,\n",
    "                                           fc_dropout=fc_dropout, bn=bn, y_range=y_range)\n",
    "        super().__init__(OrderedDict([('backbone', backbone), ('head', head)]))\n",
    "\n",
    "\n",
    "    def create_head(self, nf, c_out, seq_len, flatten=True, concat_pool=False, act=\"gelu\", fc_dropout=0., bn=False, y_range=None):\n",
    "        layers = [get_activation_fn(act)]\n",
    "        if flatten:\n",
    "            nf *= seq_len\n",
    "            layers += [Flatten()]\n",
    "        else:\n",
    "            if concat_pool: nf *= 2\n",
    "            layers = [GACP1d(1) if concat_pool else GAP1d(1)]\n",
    "        layers += [LinBnDrop(nf, c_out, bn=bn, p=fc_dropout)]\n",
    "        if y_range: layers += [SigmoidRange(*y_range)]\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "\n",
    "    def show_pe(self, cmap='viridis', figsize=None):\n",
    "        plt.figure(figsize=figsize)\n",
    "        plt.pcolormesh(self.backbone.W_pos.detach().cpu().T, cmap=cmap)\n",
    "        plt.title('Positional Encoding')\n",
    "        plt.colorbar()\n",
    "        plt.show()\n",
    "        plt.figure(figsize=figsize)\n",
    "        plt.title('Positional Encoding - value along time axis')\n",
    "        plt.plot(F.relu(self.backbone.W_pos.data).mean(1).cpu())\n",
    "        plt.plot(-F.relu(-self.backbone.W_pos.data).mean(1).cpu())\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model parameters: 470018\n"
     ]
    }
   ],
   "source": [
    "from tsai.models.utils import build_ts_model\n",
    "\n",
    "bs = 8\n",
    "c_in = 9  # aka channels, features, variables, dimensions\n",
    "c_out = 2\n",
    "seq_len = 1_500\n",
    "\n",
    "xb = torch.randn(bs, c_in, seq_len).to(device)\n",
    "\n",
    "# standardize by channel by_var based on the training set\n",
    "xb = (xb - xb.mean((0, 2), keepdim=True)) / xb.std((0, 2), keepdim=True)\n",
    "\n",
    "# Settings\n",
    "max_seq_len = 256\n",
    "d_model = 128\n",
    "n_heads = 16\n",
    "d_k = d_v = None  # if None --> d_model // n_heads\n",
    "d_ff = 256\n",
    "norm = \"BatchNorm\"\n",
    "dropout = 0.1\n",
    "activation = \"gelu\"\n",
    "n_layers = 3\n",
    "fc_dropout = 0.1\n",
    "pe = None\n",
    "learn_pe = True\n",
    "kwargs = {}\n",
    "\n",
    "model = TSTPlus(c_in, c_out, seq_len, max_seq_len=max_seq_len, d_model=d_model, n_heads=n_heads,\n",
    "                d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, dropout=dropout, activation=activation, n_layers=n_layers,\n",
    "                fc_dropout=fc_dropout, pe=pe, learn_pe=learn_pe, **kwargs).to(device)\n",
    "test_eq(model(xb).shape, [bs, c_out])\n",
    "test_eq(model[0], model.backbone)\n",
    "test_eq(model[1], model.head)\n",
    "model2 = build_ts_model(TSTPlus, c_in, c_out, seq_len, max_seq_len=max_seq_len, d_model=d_model, n_heads=n_heads,\n",
    "                           d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, dropout=dropout, activation=activation, n_layers=n_layers,\n",
    "                           fc_dropout=fc_dropout, pe=pe, learn_pe=learn_pe, **kwargs).to(device)\n",
    "test_eq(model2(xb).shape, [bs, c_out])\n",
    "test_eq(model2[0], model2.backbone)\n",
    "test_eq(model2[1], model2.head)\n",
    "print(f'model parameters: {count_parameters(model)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([False, False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_padding_mask = torch.sort(torch.randint(0, 2, (bs, max_seq_len))).values.bool().to(device)\n",
    "key_padding_mask[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 2])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.key_padding_mask = True\n",
    "model2.to(device)((xb, key_padding_mask)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): GELU()\n",
       "  (1): Flatten(full=False)\n",
       "  (2): LinBnDrop(\n",
       "    (0): Dropout(p=0.1, inplace=False)\n",
       "    (1): Linear(in_features=32768, out_features=2, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = TSTPlus(c_in, c_out, seq_len, pre_norm=True)\n",
    "test_eq(model.to(xb.device)(xb).shape, [bs, c_out])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model parameters: 605698\n"
     ]
    }
   ],
   "source": [
    "bs = 8\n",
    "c_in = 9  # aka channels, features, variables, dimensions\n",
    "c_out = 2\n",
    "seq_len = 5000\n",
    "\n",
    "xb = torch.randn(bs, c_in, seq_len)\n",
    "\n",
    "# standardize by channel by_var based on the training set\n",
    "xb = (xb - xb.mean((0, 2), keepdim=True)) / xb.std((0, 2), keepdim=True)\n",
    "\n",
    "model = TSTPlus(c_in, c_out, seq_len, res_attention=True)\n",
    "test_eq(model.to(xb.device)(xb).shape, [bs, c_out])\n",
    "print(f'model parameters: {count_parameters(model)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model parameters: 421122\n"
     ]
    }
   ],
   "source": [
    "custom_head = partial(create_pool_head, concat_pool=True)\n",
    "model = TSTPlus(c_in, c_out, seq_len, max_seq_len=max_seq_len, d_model=d_model, n_heads=n_heads,\n",
    "            d_k=d_k, d_v=d_v, d_ff=d_ff, dropout=dropout, activation=activation, n_layers=n_layers,\n",
    "            fc_dropout=fc_dropout, pe=pe, learn_pe=learn_pe, flatten=False, custom_head=custom_head, **kwargs)\n",
    "test_eq(model.to(xb.device)(xb).shape, [bs, c_out])\n",
    "print(f'model parameters: {count_parameters(model)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model parameters: 554240\n"
     ]
    }
   ],
   "source": [
    "custom_head = partial(create_pool_plus_head, concat_pool=True)\n",
    "model = TSTPlus(c_in, c_out, seq_len, max_seq_len=max_seq_len, d_model=d_model, n_heads=n_heads,\n",
    "            d_k=d_k, d_v=d_v, d_ff=d_ff, dropout=dropout, activation=activation, n_layers=n_layers,\n",
    "            fc_dropout=fc_dropout, pe=pe, learn_pe=learn_pe, flatten=False, custom_head=custom_head, **kwargs)\n",
    "test_eq(model.to(xb.device)(xb).shape, [bs, c_out])\n",
    "print(f'model parameters: {count_parameters(model)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model parameters: 421762\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): GELU()\n",
       "  (1): Flatten(full=False)\n",
       "  (2): LinBnDrop(\n",
       "    (0): Dropout(p=0.1, inplace=False)\n",
       "    (1): Linear(in_features=7680, out_features=2, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs = 8\n",
    "c_in = 9  # aka channels, features, variables, dimensions\n",
    "c_out = 2\n",
    "seq_len = 60\n",
    "\n",
    "xb = torch.randn(bs, c_in, seq_len)\n",
    "\n",
    "# standardize by channel by_var based on the training set\n",
    "xb = (xb - xb.mean((0, 2), keepdim=True)) / xb.std((0, 2), keepdim=True)\n",
    "\n",
    "# Settings\n",
    "max_seq_len = 120\n",
    "d_model = 128\n",
    "n_heads = 16\n",
    "d_k = d_v = None # if None --> d_model // n_heads\n",
    "d_ff = 256\n",
    "dropout = 0.1\n",
    "act = \"gelu\"\n",
    "n_layers = 3\n",
    "fc_dropout = 0.1\n",
    "pe='zeros'\n",
    "learn_pe=True\n",
    "kwargs = {}\n",
    "# kwargs = dict(kernel_size=5, padding=2)\n",
    "\n",
    "model = TSTPlus(c_in, c_out, seq_len, max_seq_len=max_seq_len, d_model=d_model, n_heads=n_heads,\n",
    "            d_k=d_k, d_v=d_v, d_ff=d_ff, dropout=dropout, act=act, n_layers=n_layers,\n",
    "            fc_dropout=fc_dropout, pe=pe, learn_pe=learn_pe, **kwargs)\n",
    "test_eq(model.to(xb.device)(xb).shape, [bs, c_out])\n",
    "print(f'model parameters: {count_parameters(model)}')\n",
    "body, head = model[0], model[1]\n",
    "test_eq(body.to(xb.device)(xb).ndim, 3)\n",
    "test_eq(head.to(xb.device)(body.to(xb.device)(xb)).ndim, 2)\n",
    "head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.show_pe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(32)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 10])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = TSTPlus(3, 2, 10)\n",
    "xb = torch.randn(4, 3, 10)\n",
    "yb = torch.randint(0, 2, (4,))\n",
    "test_eq(model.backbone._key_padding_mask(xb)[1], None)\n",
    "random_idxs = np.random.choice(len(xb), 2, False)\n",
    "xb[random_idxs, :, -5:] = np.nan\n",
    "xb[random_idxs, 0, 1] = np.nan\n",
    "test_eq(model.backbone._key_padding_mask(xb.clone())[1].data, (torch.isnan(xb).float().mean(1)==1).bool())\n",
    "test_eq(model.backbone._key_padding_mask(xb.clone())[1].data.shape, (4,10))\n",
    "print(torch.isnan(xb).sum())\n",
    "pred = model.to(xb.device)(xb.clone())\n",
    "loss = CrossEntropyLossFlat()(pred, yb)\n",
    "loss.backward()\n",
    "model.to(xb.device).backbone._key_padding_mask(xb)[1].data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = 4\n",
    "c_in = 3\n",
    "seq_len = 10\n",
    "c_out = 2\n",
    "xb = torch.randn(bs, c_in, seq_len)\n",
    "xb[:, -1] = torch.randint(0, 2, (bs, seq_len)).sort()[0]\n",
    "model = TSTPlus(c_in, c_out, seq_len).to(xb.device)\n",
    "test_eq(model.backbone._key_padding_mask(xb)[1], None)\n",
    "model = TSTPlus(c_in, c_out, seq_len, padding_var=-1).to(xb.device)\n",
    "test_eq(model.backbone._key_padding_mask(xb)[1], (xb[:, -1]==1))\n",
    "model = TSTPlus(c_in, c_out, seq_len, padding_var=2).to(xb.device)\n",
    "test_eq(model.backbone._key_padding_mask(xb)[1], (xb[:, -1]==1))\n",
    "test_eq(model(xb).shape, (bs, c_out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "@delegates(TSTPlus.__init__)\n",
    "class MultiTSTPlus(nn.Sequential):\n",
    "    _arch = TSTPlus\n",
    "    def __init__(self, feat_list, c_out, seq_len, max_seq_len:Optional[int]=512, custom_head=None, **kwargs):\n",
    "        r\"\"\"\n",
    "        MultiTST is a class that allows you to create a model with multiple branches of TST.\n",
    "        \n",
    "        Args:\n",
    "            * feat_list: list with number of features that will be passed to each body, or list of list with feature indices.\n",
    "        \"\"\"\n",
    "        self.feat_list = [feat_list] if isinstance(feat_list, int) else feat_list \n",
    "        self.device = ifnone(device, default_device())\n",
    "        \n",
    "        # Backbone\n",
    "        branches = nn.ModuleList()\n",
    "        self.head_nf = 0\n",
    "        for feat in self.feat_list:\n",
    "            if is_listy(feat): feat = len(feat)\n",
    "            m = build_ts_model(self._arch, c_in=feat, c_out=c_out, seq_len=seq_len, max_seq_len=max_seq_len, **kwargs)\n",
    "            with torch.no_grad(): \n",
    "                self.head_nf += m[0](torch.randn(1, feat, ifnone(seq_len, 10)).to(self.device)).shape[1]\n",
    "            branches.append(m.backbone)\n",
    "        backbone = _Splitter(self.feat_list, branches)\n",
    "        \n",
    "        # Head\n",
    "        self.c_out = c_out\n",
    "        q_len = min(seq_len, max_seq_len)\n",
    "        self.seq_len = q_len \n",
    "        if custom_head is None:\n",
    "            head = self._arch.create_head(self, self.head_nf, c_out, q_len)\n",
    "        else: \n",
    "            head = custom_head(self.head_nf, c_out, q_len)\n",
    "    \n",
    "        layers = OrderedDict([('backbone', nn.Sequential(backbone)), ('head', nn.Sequential(head))])\n",
    "        super().__init__(layers)\n",
    "        self.to(self.device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporti\n",
    "class _Splitter(Module):\n",
    "    def __init__(self, feat_list, branches):\n",
    "        self.feat_list, self.branches = feat_list, branches\n",
    "    def forward(self, x):\n",
    "        if is_listy(self.feat_list[0]): \n",
    "            x = [x[:, feat] for feat in self.feat_list]\n",
    "        else: \n",
    "            x = torch.split(x, self.feat_list, dim=1)\n",
    "        _out = []\n",
    "        for xi, branch in zip(x, self.branches): _out.append(branch(xi))\n",
    "        output = torch.cat(_out, dim=1)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = 8\n",
    "c_in = 7  # aka channels, features, variables, dimensions\n",
    "c_out = 2\n",
    "seq_len = 10\n",
    "xb2 = torch.randn(bs, c_in, seq_len)\n",
    "model1 = MultiTSTPlus([2, 5], c_out, seq_len)\n",
    "model2 = MultiTSTPlus(7, c_out, seq_len)\n",
    "test_eq(model1.to(xb2.device)(xb2).shape, (bs, c_out))\n",
    "test_eq(model1.to(xb2.device)(xb2).shape, model2.to(xb2.device)(xb2).shape)\n",
    "test_eq(count_parameters(model1) > count_parameters(model2), True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = 8\n",
    "c_in = 7  # aka channels, features, variables, dimensions\n",
    "c_out = 2\n",
    "seq_len = 10\n",
    "xb2 = torch.randn(bs, c_in, seq_len)\n",
    "model1 = MultiTSTPlus([2, 5], c_out, seq_len, )\n",
    "model2 = MultiTSTPlus([[0,2,5], [0,1,3,4,6]], c_out, seq_len)\n",
    "test_eq(model1.to(xb2.device)(xb2).shape, (bs, c_out))\n",
    "test_eq(model1.to(xb2.device)(xb2).shape, model2.to(xb2.device)(xb2).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Sequential(\n",
       "    (0): GELU()\n",
       "    (1): Flatten(full=False)\n",
       "    (2): LinBnDrop(\n",
       "      (0): Linear(in_features=2560, out_features=2, bias=True)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = MultiTSTPlus([2, 5], c_out, seq_len, y_range=(0.5, 5.5))\n",
    "body, head = split_model(model1)\n",
    "test_eq(body.to(xb2.device)(xb2).ndim, 3)\n",
    "test_eq(head.to(xb2.device)(body.to(xb2.device)(xb2)).ndim, 2)\n",
    "head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MultiTSTPlus([2, 5], c_out, seq_len, pre_norm=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 2])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): AdaptiveAvgPool1d(output_size=1)\n",
       "  (1): Flatten(full=False)\n",
       "  (2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (3): Linear(in_features=128, out_features=512, bias=False)\n",
       "  (4): ReLU(inplace=True)\n",
       "  (5): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (6): Linear(in_features=512, out_features=2, bias=False)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs = 8\n",
    "n_vars = 3\n",
    "seq_len = 12\n",
    "c_out = 2\n",
    "xb = torch.rand(bs, n_vars, seq_len)\n",
    "net = MultiTSTPlus(n_vars, c_out, seq_len)\n",
    "change_model_head(net, create_pool_plus_head, concat_pool=False)\n",
    "print(net.to(xb.device)(xb).shape)\n",
    "net.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 5, 2])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): create_conv_lin_3d_head(\n",
       "    (0): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (1): Conv1d(128, 5, kernel_size=(1,), stride=(1,), bias=False)\n",
       "    (2): Transpose(-1, -2)\n",
       "    (3): BatchNorm1d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (4): Transpose(-1, -2)\n",
       "    (5): Linear(in_features=12, out_features=2, bias=False)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs = 8\n",
    "n_vars = 3\n",
    "seq_len = 12\n",
    "c_out = 10\n",
    "xb = torch.rand(bs, n_vars, seq_len)\n",
    "new_head = partial(conv_lin_3d_head, d=(5 ,2))\n",
    "net = MultiTSTPlus(n_vars, c_out, seq_len, custom_head=new_head)\n",
    "print(net.to(xb.device)(xb).shape)\n",
    "net.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.save_checkpoint();"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108c_models.TSTPlus.ipynb saved at 2021-11-24 13:15:04.\n",
      "Converted 108c_models.TSTPlus.ipynb.\n",
      "\n",
      "\n",
      "Correct conversion! 😃\n",
      "Total time elapsed 0.094 s\n",
      "Wednesday 24/11/21 13:15:07 CET\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide\n",
    "from tsai.imports import create_scripts\n",
    "from tsai.export import get_nb_name\n",
    "nb_name = get_nb_name()\n",
    "create_scripts(nb_name);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
